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Value-at-Risk and Extreme Returns

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  • Jon Danielsson
  • Casper G. De Vries

Abstract

We propose a semi-parametric method for unconditional Value-at-Risk (VaR) evaluation. The largest risks are modelled parametrically, while smaller risks are captured by the non-parametric empirical distribution function. A comparison of methods on a portfolio of stock and option returns reveals that at the 5% level the RiskMetrics analysis is best, but for predictions of low probability worst outcomes, it strongly underpredicts the VaR while the semi-parametric method is the most accurate.

Suggested Citation

  • Jon Danielsson & Casper G. De Vries, 2000. "Value-at-Risk and Extreme Returns," Annals of Economics and Statistics, GENES, issue 60, pages 239-270.
    • Handle: RePEc:adr:anecst:y:2000:i:60:p:239-270
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    File URL: http://www.jstor.org/stable/20076262
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    References listed on IDEAS

    as
    1. J. S. Butler & Barry Schachter, 1996. "Improving Value-At-Risk Estimates By Combining Kernel Estimation With Historical Simulation," Finance 9605001, University Library of Munich, Germany.
    2. J. S. Butler & Barry Schachter, 1996. "Improving value-at-risk estimates by combining kernel estimation," Proceedings 513, Federal Reserve Bank of Chicago.
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    More about this item

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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